lstm

来源： https://stackoverflow.com/questions/41972573/tensorflow-jointly-training-cnn-lstm

来源： https://stackoverflow.com/questions/63475694/prediction-for-multi-variate-input-lstm-or-lstm-conv1d-network-way-off-track

来源： https://stackoverflow.com/questions/64138693/how-to-enter-data-to-lstmcnn-network

来源： https://stackoverflow.com/questions/64138693/how-to-enter-data-to-lstmcnn-network

来源： https://stackoverflow.com/questions/63809805/build-pre-trainedcnnlstm-network-with-keras-functional-api

来源： https://stackoverflow.com/questions/63987125/keras-lstm-vae-variational-autoencoder-for-time-series-anamoly-detection

来源： https://stackoverflow.com/questions/63987125/keras-lstm-vae-variational-autoencoder-for-time-series-anamoly-detection

来源： https://stackoverflow.com/questions/48850424/building-an-lstm-net-with-an-embedding-layer-in-keras

来源： https://stackoverflow.com/questions/61149523/understanding-the-structure-of-my-lstm-model

问题 So I am trying to build an LSTM based autoencoder, which I want to use for the time series data. These are spitted up to sequences of different lengths. Input to the model has thus shape [None, None, n_features], where the first None stands for number of samples and the second for time_steps of the sequence. The sequences are processed by LSTM with argument return_sequences = False, coded dimension is then recreated by function RepeatVector and ran through LSTM again. In the end I would like